A natural scene typically includes smoothly changing areas where the luminance is changing gradually. When the scene is recorded by a digital device such as a digital video camera, such digitization (i.e., quantization) of the luminance in a smoothly changing area may generate quantization artifacts, whereby the area no longer looks smooth to the human eye.
Quantization artifacts are perceivable by the human visual system. The magnitude of quantization artifacts is determined by the quantization accuracy in an analog-to-digital (A/D) converter of the digitization device. This magnitude is not necessarily the same as the smallest step of the digitization device, and is typically much larger, which makes the quantization artifacts more obvious to the human eye.
To eliminate such quantization artifacts, their locations in a digitized image must first be identified. Then, based on the assumption that such locations belong to originally smoothly changing areas, a smoothing operation is performed on the quantization artifacts. In general, within a largely slowly changing region the quantization artifacts resemble steps, however identifying them in a digitized image is a difficult task as it must be determined whether the artifacts are caused by the quantization of smoothly changing areas or are part of the original scene.
Such identification becomes more complicated in the presence of additive noise, which is introduced by the digitization (e.g., recording) device. Such noise makes areas containing quantization artifacts look like small detailed regions in the original scene. Conventionally, when a noisy area is identified as an area containing quantization artifacts, then a smoothing process is applied to reduce the noise, as well as the image quantization layer (or the bit-plane).
Further, a smoothly changing area may look stage-like even when the luminance of neighboring pixels is only changing by the smallest possible step. In this case, a higher precision content of the smoothly changing area is desired in order to eliminate the quantization artifacts. With the higher precision content, halftoning techniques such as error diffusion or spatial dithering can be used to quantize the higher precision content to the current bit depth. The quantization artifacts will no longer be perceivable on the halftoned image due to the spatial averaging characteristics of the human visual system. There is, therefore, a need for a method and a system for reducing the quantization layer in a quantized image.